AI Threat Alert indexes 4,319+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
Showing 841–860 of 4,319 papers
Avital Aviv, Parth A. Gandh, Ron Bitton +1 more
The Agent Payments Protocol (AP2), introduced by Google, enables large language model (LLM)-driven shopping agents to authorize and execute payments...
Trung-Anh Dang, Duy-Cuong Bui, Ngoc-Son Vu +2 more
Continual learning faces the persistent challenge of catastrophic forgetting, where sequential task updates degrade previously acquired knowledge....
Mehrdad Rostamzadeh, Sidhant Narula, Mohammad Ghasemigol +1 more
The Model Context Protocol (MCP) has emerged as the standard layer connecting Large Language Model agents to external tool backends. This openness...
Mustafa Umut Ozbek, Taiwo Ojo, Pooria Madani +2 more
Machine-learning-based anomaly detection is increasingly used in industrial control systems (ICS), yet most studies assume that detector training...
Hanling Tian, Gengyu Zhang, Zeyang Sha +5 more
Memory is becoming a default subsystem in deployed LLM agents to provide persistent personalization and continuity. This naturally prompts a...
Seokjin Hwang, Yuting, Li +1 more
Instance encoding is a popular empirical technique for privacy enhancement when sharing data to an untrusted server. It transforms sensitive data...
Seokjin Hwang, Yuting Li, Kiwan Maeng
Instance encoding is a popular empirical technique for privacy enhancement when sharing data to an untrusted server. It transforms sensitive data...
Nikita Kezins
Gumbel-based inference verification bounds LLM weight exfiltration by only forgiving token choices that plausibly arise from honest GPU...
Aldo Gangemi, Emanuele Bottazzi
Large Language Models (LLMs) recognise patterns but do not natively track the path of exclusions that a coherent discourse demands. When an input...
Pankaj Kumar, Judith T. Karpen, David Lario +4 more
Understanding how energetic particles are accelerated and released from the low corona into the interplanetary medium during solar eruptions is...
Or Biton, Tomer Krichli, Itai Allouche +1 more
Although Large Language Models (LLMs) are aligned to optimize for both helpfulness and harmlessness, these dual objectives may conflict, inevitably...
Lorenzo Bossi, Federico Saccani, Francesco Panebianco +4 more
Cyber threat intelligence from underground forums has traditionally relied on passive monitoring. However, as users have become more aware of...
Jian Yang, Haau-Sing Li, Shawn Guo +8 more
As large language models (LLMs) continue to advance in coding capabilities, their potential in cybersecurity has drawn increasing research attention,...
Jian Yang, Haau-Sing Li, Shawn Guo +9 more
As large language models (LLMs) continue to advance in coding capabilities, their potential in cybersecurity has drawn increasing research attention,...
Xiangxin Zhang, Zhanwei Zhang, Zhihang Fu +2 more
Web search agents powered by Large Language Models (LLMs) show strong promise, but deep research tasks expose a recurring failure mode: once an agent...
Zeyu Feng, Qingyu Wu, Yuzhe Luo +1 more
Large language models (LLMs) are increasingly deployed in education, healthcare, policy advising, and other interactive settings, where users engage...
Kohei Yamamoto, Marie Katsurai
Attacks against Internet-connected IoT devices continue to increase; however, transforming observed attack traffic into deployable intrusion...
Basavesh Ammanaghatta Shivakumar, Swarn Priya, Peng Gao
LLM agents increasingly read untrusted content, invoke external tools, access private data, and delegate work to other agents. Harm often arises not...
Saber Zerhoudi, Jelena Mitrovic, Michael Granitzer
A safety rule and an episodic log compete for the same tokens in an AI agent's context. When the budget overflows, both are summarized at the same...
Isaac Kofi Nti
Machine-learning malware detectors often achieve high clean-data accuracy, but operational triage also requires evidence about uncertainty, novelty,...
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
AI Threat Alert indexes 4,319+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
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